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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import fastai\n",
"import torch\n",
"import torchvision\n",
"import os\n",
"from torchvision import transforms\n",
"from fastai.vision.all import *"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"#prepare data loader\n",
"aug_tfm = aug_transforms(\n",
" mult=1.5, do_flip=False, p_affine=0.75, pad_mode=\"zeros\"\n",
" )\n",
"data_source = Path(\"./data/generated\")\n",
"dls = ImageDataLoaders.from_folder(\n",
" data_source, valid_pct=0.2, item_tfms=Resize(224), batch_tfms=aug_tfm\n",
" )\n",
"dls.show_batch()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"#prepare learner on pretrained resnet18 base\n",
"learn = vision_learner(dls, resnet18, metrics=[error_rate, accuracy])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#load pretrained weights\n",
"learn.load('pretrained')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#saves pretrained weights only (without optimizer state, as pth file)\n",
"learn.save('pretrained', with_opt=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"learn.fine_tune(3)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"learn.export('model.pkl')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.13 ('.venv': venv)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "44cab64d7dc7485631db53a8a9ba4dbc7180e4632dd35e38622d9cb1ff9be0a7"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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